code: def downsample_basic_block(inplanes, outplanes, stride):
      """downsample_basic_block.
      :param inplanes: int, number of channels in the input sequence.
      :param outplanes: int, number of channels produced by the convolution.
      :param stride: int, size of the convolving kernel.
      """
      return nn.Sequential(
          nn.Conv2d(
              inplanes,
              outplanes,
              kernel_size=1,
              stride=stride,
              bias=False,
          ),
          nn.BatchNorm2d(outplanes),
      )
  
file_path: audio/examples/avsr/models/resnet.py
imports: import torch.nn
label_desc: Use image node_id 2 for calling a global function with example usage: downsample_basic_block(inplanes, outplanes, stride) and returns: nn